| Name: | EinsteinPy |
|---|---|
| Website: | https://einsteinpy.org/ |
| Version: | 0.5.dev0 |
EinsteinPy is an open-source pure Python package dedicated to the study of problems arising in General Relativity and gravitational physics. Using EinsteinPy, it is possible to approach problems symbolically as well as numerically. On the symbolic side, EinsteinPy provides a robust API, which allows users to access several predefined metrics or to define custom metrics and perform symbolic calculations on them. Computation of quantities, such as terms of metric tensors, Christoffel symbols, Riemann Curvature tensor, Ricci tensor, stress-energy tensor and more are all supported and extensible, since the symbolic modules are built on top of SymPy. On the numerical side, EinsteinPy provides tools to calculate and visualize geodesics, metric singularities and hypersurface embeddings in certain spacetimes. We hope to extend the package to include more features in the future. EinsteinPy is released under the MIT license.
Complete documentation, including a user guide and an API reference, can be perused on the wonderful Read the Docs.
Several tutorial Jupyter notebooks on specific applications of EinsteinPy can be found in the examples directory. You can launch a Jupyter notebook instance in the cloud using binder to run and edit these notebooks without installing anything. Try it out!
EinsteinPy requires the following Python packages:
NumPy, for basic numerical routinesSciPy, for solving ordinary differential equationsSymPy, for symbolic calculationsAstropy, for handling conversion between physical unitsMatplotlib, for producing static visualizationsPlotly, for producing interactive visualizationsNumba, for accelerating the code
EinsteinPy is currently tested on Linux, Windows and macOS on Python 3.7 and 3.8, against the latest NumPy.
| Platform | Site | Status |
|---|---|---|
| Linux | CircleCI | |
| macOS | Github Actions | |
| Windows x64 | Appveyor | |
Currently, the recommended way to install EinsteinPy is using pip
from PyPI:
$ pip install einsteinpy
Or, you can install the package using conda:
$ conda install einsteinpy --channel conda-forge
Note that the package on conda-forge is currently a version behind PyPI. We are working on updating it.
For Debian/Ubuntu/Mint users, the package is installable via apt (Ubuntu 19.04 onwards):
$ sudo apt install python3-einsteinpy
If you prefer to install from source to stay on the latest but likely unstable version, you can do so using the method described here.
If the installation fails or you find something that doesn't work as expected, please open an issue in the issue tracker.
EinsteinPy is a community project. Hence, all contributions are more than welcome! For more information, head to CONTRIBUTING or see the developer guide.
Please join our [matrix] channel or Gitter chat room for general discussions and further queries.
Release announcements take place on our mailing list. Feel free to join!
If you still have a doubt, write to us directly at all@einsteinpy.org.
If you use EinsteinPy in your project, please drop us a line. You can also use the DOI to cite it in your publications. This is the latest one:
And this is an example citation format:
Shreyas Bapat et al (2021). EinsteinPy 0.4.0 (v0.4.0). Zenodo. https://doi.org/10.5281/zenodo.2582387
EinsteinPy is released under the MIT license, thereby allowing commercial use of the library. Please refer to COPYING for more details.
EinsteinPy borrows the name of the famous physicist, Nobel laureate and revolutionary human, Dr. Albert Einstein. This is a small tribute on our part for the amazing work he did for humanity!
EinsteinPy is focused on general relativity. One can always discuss probable features in discussion forums and the mailing list and also work with the maintainers to try to implement them. We welcome every contribution to EinsteinPy. Please see CONTRIBUTING for more details.
EinsteinPy is actively maintained and we hope to receive an influx of new contributors. The best way to get an idea about the roadmap is to view the milestones of the project.
The documentation and code structure is shamelessly inspired by poliastro. We wholeheartedly thank the poliastro
developers that made this possible. EinsteinPy is nothing without its supporters and community.